Sparsifying the Update Step in Graph Neural Networks. (arXiv:2109.00909v2 [cs.LG] UPDATED)

Message-Passing Neural Networks (MPNNs), the most prominent Graph Neural
Network (GNN) framework, celebrate much success in the analysis of
graph-structured data. Concurrently, the sparsification of Neural Network
models attracts a great amount of academic and industrial interest. In this
paper, we conduct a structured study of the effect of sparsification on the
trainable part of MPNNs known as the Update step. To this end, we design a
series of models to successively sparsify the linear transform in the Update
step. …

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